Computed tomography (CT) systems and methods are widely used, particularly for medical imaging and diagnosis. CT systems generally create images of one or more sectional slices through a subject's body. A radiation source, such as an X-ray source, irradiates the body from one side. At least one detector on the opposite side of the body receives radiation transmitted through the body. The attenuation of the radiation that has passed through the body is measured by processing electrical signals received from the detector.
A CT sinogram indicates attenuation through the body as a function of position along a detector array and as a function of the projection angle between the X-ray source and the detector array for various projection measurements. In a sinogram, the spatial dimensions refer to the position along the array of X-ray detectors. The time/angle dimension refers to the projection angle of X-rays, which changes as a function of time during a CT scan. The attenuation resulting from a portion of the imaged object (e.g., a vertebra) will trace out a sine wave around the vertical axis. Those portions farther from the axis of rotation correspond to sine waves with larger amplitudes, and the phase of the sine waves correspond to the angular positions of objects around the rotation axis. Performing an inverse Radon transform—or any other image reconstruction method—reconstructs an image from the projection data in the sinogram.
In some CT modalities, such as in a profusion study, a time series of reconstructed images can be obtained. Additionally, in spectral CT, X-rays having various energies are propagated through a patient and variously detected (e.g., using an energy resolving detector such as a photon-counting detector) and reconstructed images can be obtained at each respective energy. Alternatively, the energy resolved projection data can be decomposed into material components corresponding to high-Z atoms and low-Z atoms. For example, in a patient image the two material components can be bone and water, wherein the water component includes tissues and fluid primarily composed of water such as blood and soft tissue.
To obtain the spectral nature of the transmitted X-ray data, the photon-counting detectors differentiate between the X-rays having different energies by resolving detected X-rays into energy bins and counting the number of X-rays in each of the bins along each detector element of the detector array. Since spectral CT involves the detection of transmitted X-rays at two or more energy levels, spectral CT generally includes dual-energy CT by definition. Because different materials (i.e., materials having high-Z atoms and low-Z atoms, respectively) exhibit different spectral attenuation signatures for X-rays, spectral-CT projection data can be decomposed into material components using a material-decomposition method. The material-component images can then be reconstructed from material-component sinograms.
Whether spectral CT sinograms or reconstructed images are expressed using an energy-bin basis or a material-component basis, the sinograms and/or images can be thought of a three-dimensional representations, snapshots, or constituents of a four-dimensional image in which the fourth dimension is either X-ray energy or material component.
Similarly, in certain medical imaging modalities, CT images can be taken at a series of times to observe changes as a function of time. For example, in a profusion study, a contrast agent can be injected into a blood stream of a patient shortly after a first CT scan in a time series of multiple CT scans to observe the dynamics of blood flow through the patient. In this case, the sinograms and/or reconstructed images are three-dimensional constituents of a four-dimensional image having time as its fourth dimension.
In both cases mentioned above and many others involving four-dimensional images in medical imaging, the three-dimensional constituents of a four-dimensional image have significant common features and structure among the respective three-dimensional constituents. Thus, conventional denoising methods, which denoise the respective three-dimensional constituents separately, are underutilizing common information and signals among the respective three-dimensional constituents that could be used to denoise the three-dimensional constituents without sacrificing resolution in order to improve the image quality of the four-dimensional images.